code: def predict(self, X):
      row = X[-self.n_past :].flatten()
      y_pred = self.model.predict(np.asarray([row]))
      return y_pred
  
file_path: AIX360/tests/tslime/test_tslime.py
imports: import os
  import unittest
  import numpy
  import pandas
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import RandomForestRegressor
  from aix360.algorithms.tsutils.tsframe import tsFrame
  from aix360.datasets import SunspotDataset
  from aix360.algorithms.tslime import TSLimeExplainer
  from aix360.algorithms.tsutils.tsperturbers import BlockBootstrapPerturber
label_desc: Use image node_id 4 for calling the RandomForestUniVariateForecaster obj's underlying member method code with example usage: obj.predict(X) and returns: y_pred